Evidence map›Paper›PMID 38918718›Full record

ArticleBMC infectious diseases2024

Global patterns of rebound to normal RSV dynamics following COVID-19 suppression.

Deus Thindwa, Ke Li, Dominic Cooper-Wootton, Zhe Zheng, Virginia E Pitzer, Daniel M Weinberger

Abstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Deus ThindwaDepartment of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA. deus.thindwa@yale.edu.ORCID http://orcid.org/0000-0003-4845-7862
Ke LiDepartment of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-1252-670X
Dominic Cooper-WoottonDepartment of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.ORCID http://orcid.org/0009-0001-0080-5680
Zhe ZhengDepartment of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-1869-8529
Virginia E PitzerDepartment of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.ORCID http://orcid.org/0000-0003-1015-2289
Daniel M WeinbergerDepartment of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA. daniel.weinberger@yale.edu.ORCID http://orcid.org/0000-0003-1178-8086

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Predicting and monitoring variations in the effects of vaccines against RSVR01AI137093 · NIAID · YALE UNIVERSITY · PI Virginia E Pitzer, Daniel Martin Weinberger · 2018 to 2026
$3.4M
NCATS NIH HHS UL1 TR001863NIAID NIH HHS R01 AI137093NIH HHS R01AI137093
6 · The paper itself

Abstract

backgroundAnnual epidemics of respiratory syncytial virus (RSV) had consistent timing and intensity between seasons prior to the SARS-CoV-2 pandemic (COVID-19). However, starting in April 2020, RSV seasonal activity declined due to COVID-19 non-pharmaceutical interventions (NPIs) before re-emerging after relaxation of NPIs. We described the unusual patterns of RSV epidemics that occurred in multiple subsequent waves following COVID-19 in different countries and explored factors associated with these patterns.

methodsWeekly cases of RSV from twenty-eight countries were obtained from the World Health Organisation and combined with data on country-level characteristics and the stringency of the COVID-19 response. Dynamic time warping and regression were used to cluster time series patterns and describe epidemic characteristics before and after COVID-19 pandemic, and identify related factors.

resultsWhile the first wave of RSV epidemics following pandemic suppression exhibited unusual patterns, the second and third waves more closely resembled typical RSV patterns in many countries. Post-pandemic RSV patterns differed in their intensity and/or timing, with several broad patterns across the countries. The onset and peak timings of the first and second waves of RSV epidemics following COVID-19 suppression were earlier in the Southern than Northern Hemisphere. The second wave of RSV epidemics was also earlier with higher population density, and delayed if the intensity of the first wave was higher. More stringent NPIs were associated with lower RSV growth rate and intensity and a shorter gap between the first and second waves.

conclusionPatterns of RSV activity have largely returned to normal following successive waves in the post-pandemic era. Onset and peak timings of future epidemics following disruption of normal RSV dynamics need close monitoring to inform the delivery of preventive and control measures.

Indexed as

COVID-19Global HealthRespiratory Syncytial Virus InfectionsSARS-CoV-2HumansPandemicsRespiratory Syncytial Virus, HumanSeasonsDynamic time warpingEpidemic onsetEpidemic peakEpidemic reboundRespiratory syncytial virus

Identifiers

PMID38918718
PMCPMC11201371

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.